Lista del top 20 de hashtags más usados y su frecuencia
Code
# convert dataframe column to listhashtags = df['hashtags'].to_list()# remove nan items from listhashtags = [x for x in hashtags ifnot pd.isna(x)]# split items into a list based on a delimiterhashtags = [x.split('|') for x in hashtags]# flatten list of listshashtags = [item for sublist in hashtags for item in sublist]# count items on listhashtags_count = pd.Series(hashtags).value_counts()# return first n rows in descending ordertop_hashtags = hashtags_count.nlargest(20)top_hashtags
# filter column from dataframeusers = df['mentioned_names'].to_list()# remove nan items from listusers = [x for x in users ifnot pd.isna(x)]# split items into a list based on a delimiterusers = [x.split('|') for x in users]# flatten list of listsusers = [item for sublist in users for item in sublist]# count items on listusers_count = pd.Series(users).value_counts()# return first n rows in descending ordertop_users = users_count.nlargest(20)top_users
# plot the data using plotlyfig = px.line(df, x='date', y='like_count', title='Número de likes en el tiempo', template='plotly_white', hover_data=['text'])# show the plotfig.show()
Tokens
Lista del top 20 de los tokens más comunes y su frecuencia
Code
# load the spacy model for Spanishnlp = spacy.load("es_core_news_sm")# load stop words for SpanishSTOP_WORDS = nlp.Defaults.stop_words# Function to filter stop wordsdef filter_stopwords(text):# lower text doc = nlp(text.lower())# filter tokens tokens = [token.text for token in doc ifnot token.is_stop and token.text notin STOP_WORDS and token.is_alpha]return' '.join(tokens)# apply function to dataframe columndf['text_pre'] = df['text'].apply(filter_stopwords)# count items on columntoken_counts = df["text_pre"].str.split(expand=True).stack().value_counts()[:20]token_counts
vida 2070
aborto 1097
colombia 719
sialavida 661
colombiaesprovida 437
mayo 390
q 388
noalaborto 370
eutanasia 323
derecho 323
gracias 309
provida 308
muerte 268
feliz 268
d 263
voz 250
mujer 222
familia 210
mujeres 204
concepción 191
Name: count, dtype: int64
Hora
Lista de las 10 horas con más cantidad de tweets publicados
Code
# extract hour from datetime columndf['hour'] = df['date'].dt.strftime('%H')# count items on columnhours_count = df['hour'].value_counts()# return first n rows in descending ordertop_hours = hours_count.nlargest(10)top_hours
Plataformas desde las que se publicaron contenidos y su frecuencia
Code
df['source_name'].value_counts()
source_name
Twitter for iPhone 2031
Twitter Web App 1706
Twitter Web Client 1487
Facebook 1468
Twitter for Android 412
Mobile Web 163
TweetDeck 133
erased88075 131
Twitter for Websites 124
Instagram 99
UberSocial for iPhone 22
Mobile Web (M2) 12
iOS 11
Twitter for Android Tablets 10
Twitter for Mac 7
Tweeet! on iOS 4
Hootsuite Inc. 3
Buffer 3
Hootsuite 2
Twibbon 1
Periscope 1
Name: count, dtype: int64
Tópicos
Técnica de modelado de tópicos con transformers y TF-IDF
Code
# visualize topicstopic_model.visualize_topics()
Reducción de tópicos
Mapa con 10 tópicos del contenido de los tweets
Code
# visualize topicstopic_model.visualize_topics()
Términos por tópico
Code
topic_model.visualize_barchart(top_n_topics=11)
Análisis de tópicos
Selección de tópicos que tocan temas de género
Code
# selection of topicstopics = [0, 1, 2]keywords_list = []for topic_ in topics: topic = topic_model.get_topic(topic_) keywords = [x[0] for x in topic] keywords_list.append(keywords)# flatten list of listsword_list = [item for sublist in keywords_list for item in sublist]# use apply method with lambda function to filter rowsfiltered_df = df[df['text_pre'].apply(lambda x: any(word in x for word in word_list))]percentage =round(100*len(filtered_df) /len(df), 2)print(f" Del total de {len(df)} tweets de @UnidosxlaVidaCo, alrededor de {len(filtered_df)} hablan sobre temas de género, es decir, cerca del {percentage}%")
Del total de 7830 tweets de @UnidosxlaVidaCo, alrededor de 5573 hablan sobre temas de género,
es decir, cerca del 71.17%
Code
# drop rows with 0 values in two columnsfiltered_df = filtered_df[(filtered_df.like_count !=0) & (filtered_df.retweet_count !=0)]# add a new column with the sum of two columnsfiltered_df['impressions'] = (filtered_df['like_count'] + filtered_df['retweet_count'])/2# extract year from datetime columnfiltered_df['year'] = filtered_df['date'].dt.year# remove urls, mentions, hashtags and numbersp.set_options(p.OPT.URL)filtered_df['tweet_text'] = filtered_df['text'].apply(lambda x: p.clean(x))# Create scatter plotfig = px.scatter(filtered_df, x='like_count', y='retweet_count', size='impressions', color='year', hover_name='tweet_text')# Update title and axis labelsfig.update_layout( title='Tweets talking about gender with most Likes and Retweets', xaxis_title='Number of Likes', yaxis_title='Number of Retweets')fig.show()